Instantly figure out risks with new configs while developing a new microservice
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Minimized failures due to the way Autoscaler behaves with all of the above – e.g. simulated load on specific service may trigger random evictions, freezes, or fail to perform scaling due to constraints/missing nodes that have requested resources.
Minimized failures due to resource limits mistakes. Various scenarios with setting incorrect constraints of limits/requests/taints/affinity – e.g. creating a DaemonSet with limits bound to nodes with pods with no limits will block the cluster from starting any pods at all; and will evict and kill the cluster if podPriority is set to “critical” (we have a full model of RAM/CPU resources that covers all possible variations)
Minimized failures due to misconfigured Cron jobs. Cases like when the job takes longer to complete than it is scheduled to run with no/incorrect concurrency Policy set, combined with all of the eviction/freeze scenarios with the above.
Minimized failures due to resource utilization mishandling. Various scenarios covered, when you have too many idling pods on a node with a history of consuming lots of RAM, or when you forget to define appropriate anti-affinity – e.g. you create a new job, the pod gets scheduled on a machine with high RAM utilization, renders node unusable, gets restarted on new node, kills that too, etc.... (we have a full model of restart policy and real resource consumption that is getting filled with linked data when available).
Minimized failures due to incorrect image pull policies (static, in the process of expanding to dynamic model).These benefits are available right away and they are modeled in our AI planning domain that covers substantially more failure scenarios than is possible by manually writing each policy.
"The capabilities of the AI planning in Enterprise is unmatched, and guys at CriticalHop have proven that automation with commercial AI planning is unavoidable."
This could be a "must have" feature for every Kubernetes installation.
“NextHop can now automate Cloud Policies, Kubernetes validation, and deploy features with up to 10 times faster. Very impressed.”